Data Cleaning in R: 2 R Packages to Clean and Validate Datasets {https://t.co/MibZ2lNmMS} #rstats #DataScience
— R-bloggers (@Rbloggers) May 3, 2022
Mastering Git and GitHub: the hands-on workshop {https://t.co/1ltLOvfBBM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
The basics of prototyping and exporting your plots in R {https://t.co/1r6nIkXBl1} #rstats #DataScience
— R-bloggers (@Rbloggers) May 6, 2022
Model evaluation with presence points and raster predictions {https://t.co/k54Ut1abRJ} #rstats #DataScience
— R-bloggers (@Rbloggers) May 6, 2022
Storytelling in ggplot using rounded rectangles {https://t.co/aPOAp1Mi4P} #rstats #DataScience
— R-bloggers (@Rbloggers) May 4, 2022
New R job: Data Management Education Specialist https://t.co/cbuiVyYs3F #rstats #DataScience #jobs
— R-bloggers (@Rbloggers) May 1, 2022
New Workshops Series Kick-off {https://t.co/uaw1IPBqfT} #rstats #DataScience
— R-bloggers (@Rbloggers) May 2, 2022
Carry and Roll-Down on a Yield Curve using R code {https://t.co/k5XnSSFzSV} #rstats #DataScience
— R-bloggers (@Rbloggers) May 3, 2022
The Success Paradox: Why even a little Bit of Luck often beats Skill {https://t.co/w300T2tz6J} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
New R job: Associate Computational Scientist https://t.co/788cZIA9Jo #rstats #DataScience #jobs
— R-bloggers (@Rbloggers) May 1, 2022
Separating Code from Presentation in Jupyter Notebooks {https://t.co/CyGJnVSs2F} #rstats #DataScience
— R-bloggers (@Rbloggers) May 1, 2022
Color-Swapping Film Palettes in R with imager, ggplot2, and kmeans {https://t.co/tQNC1Cf02O} #rstats #DataScience
— R-bloggers (@Rbloggers) May 7, 2022
Bayesian analyses made easy: GLMMs in R package brms {https://t.co/POSpLZRPa1} #rstats #DataScience
— R-bloggers (@Rbloggers) April 18, 2022
R and Excel: How to Combine the Best of Both Worlds {https://t.co/29X4eIvwp3} #rstats #DataScience
— R-bloggers (@Rbloggers) April 29, 2022
Data Cleaning in R: 2 R Packages to Clean and Validate Datasets {https://t.co/MibZ2lNmMS} #rstats #DataScience
— R-bloggers (@Rbloggers) May 3, 2022
Mastering Git and GitHub: the hands-on workshop {https://t.co/1ltLOvfBBM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
How to create your own functions in R {https://t.co/2AEuycBrGe} #rstats #DataScience
— R-bloggers (@Rbloggers) April 21, 2022
Using Shiny to Create an Academic Poster {https://t.co/LUZi8zMda5} #rstats #DataScience
— R-bloggers (@Rbloggers) April 25, 2022
Text Analysis of Job Descriptions for Data Scientists, Data Engineers, Machine Learning {https://t.co/luWLNT9vV8} #rstats #DataScience
— R-bloggers (@Rbloggers) April 18, 2022
WTF is Kubernetes and Should I Care as R User? {https://t.co/Yt6tT5g15M} #rstats #DataScience
— R-bloggers (@Rbloggers) April 17, 2022
How to reshape your data in R for analysis {https://t.co/j0N0QRuVk4} #rstats #DataScience
— R-bloggers (@Rbloggers) April 28, 2022
Propensity Score Matching {https://t.co/j62sAAqC5M} #rstats #DataScience
— R-bloggers (@Rbloggers) April 12, 2022
Getting started with Python using R and reticulate {https://t.co/fQipgV3dVk} #rstats #DataScience
— R-bloggers (@Rbloggers) April 19, 2022
Linking R and Python to retrieve financial data and plot a candlestick {https://t.co/52cScE2TqA} #rstats #DataScience
— R-bloggers (@Rbloggers) April 18, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```